Long COVID and episodic disability: advancing the conceptualisation, measurement and knowledge of episodic disability among people living with Long COVID – protocol for a mixed-methods study
Bibliographic record
Abstract
INTRODUCTION: As the prevalence of Long COVID increases, there is a critical need for a comprehensive assessment of disability. Our aims are to: (1) characterise disability experiences among people living with Long COVID in Canada, UK, USA and Ireland; and (2) develop a patient-reported outcome measure to assess the presence, severity and episodic nature of disability with Long COVID. METHODS AND ANALYSIS: In phase 1, we will conduct semistructured interviews with adults living with Long COVID to explore experiences of disability (dimensions, uncertainty, trajectories, influencing contextual factors) and establish an episodic disability (ED) framework in the context of Long COVID (n~10 each country). Using the conceptual framework, we will establish the Long COVID Episodic Disability Questionnaire (EDQ). In phase 2, we will examine the validity (construct, structural) and reliability (internal consistency, test-retest) of the EDQ for use in Long COVID. We will electronically administer the EDQ and four health status criterion measures with adults living with Long COVID, and readminister the EDQ 1 week later (n~170 each country). We will use Rasch analysis to refine the EDQ, and confirm structural and cross-cultural validity. We will calculate Cronbach's alphas (internal consistency reliability), and intraclass correlation coefficients (test-retest reliability), and examine correlations for hypotheses theorising relationships between EDQ and criterion measure scores (construct validity). Using phase 2 data, we will characterise the profile of disability using structural equation modelling techniques to examine relationships between dimensions of disability and the influence of intrinsic and extrinsic contextual factors. This research involves an academic-clinical-community partnership building on foundational work in ED measurement, Long COVID and rehabilitation. ETHICS AND DISSEMINATION: This study was approved by the University of Toronto Research Ethics Board. Knowledge translation will occur with community collaborators in the form of presentations and publications in open access peer-reviewed journals and presentations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.084 | 0.072 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.053 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".